
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase     :	str       			=  logging.get_logger(__name__)


__lowerCAmelCase     :	Optional[int]       			=  {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

__lowerCAmelCase     :	int       			=  {
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
}

__lowerCAmelCase     :	List[Any]       			=  {
    'allenai/longformer-base-4096': 4096,
    'allenai/longformer-large-4096': 4096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4096,
}

@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def a__       (			):






  '''simple docstring'''





  __magic_name__								=			(
      list(range(ord("""!"""   ),	ord("""~"""   ) + 1   )   ) + list(range(ord("""¡"""   ),	ord("""¬"""   ) + 1   )   ) + list(range(ord("""®"""   ),	ord("""ÿ"""   ) + 1   )   )
  )
  __magic_name__								=			bs[:]
  __magic_name__								=			0
  for b in range(2**8   ):
    if b not in bs:
      bs.append(A_   )
      cs.append(2**8 + n   )
      n += 1
  __magic_name__								=			[chr(A_   ) for n in cs]
  return dict(zip(A_,	A_   )   )

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			set()
  __magic_name__								=			word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)   )
    __magic_name__								=			char
  return pairs





class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						VOCAB_FILES_NAMES
  a__       			=						PRETRAINED_VOCAB_FILES_MAP
  a__       			=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a__       			=						["""input_ids""", """attention_mask"""]
  def __init__(					self     :					List[str]     ,				UpperCamelCase__     :					int     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Optional[Any]="replace"     ,				UpperCamelCase__     :					Tuple="<s>"     ,				UpperCamelCase__     :					List[Any]="</s>"     ,				UpperCamelCase__     :					List[Any]="</s>"     ,				UpperCamelCase__     :					List[str]="<s>"     ,				UpperCamelCase__     :					Any="<unk>"     ,				UpperCamelCase__     :					Optional[int]="<pad>"     ,				UpperCamelCase__     :					Union[str, Any]="<mask>"     ,				UpperCamelCase__     :					int=False     ,				**UpperCamelCase__     :					str     ,				)     ->      Dict:







    """simple docstring"""






    __magic_name__								=			AddedToken(UpperCamelCase__     ,				lstrip=UpperCamelCase__     ,				rstrip=UpperCamelCase__       ) if isinstance(UpperCamelCase__     ,				UpperCamelCase__       ) else bos_token
    __magic_name__								=			AddedToken(UpperCamelCase__     ,				lstrip=UpperCamelCase__     ,				rstrip=UpperCamelCase__       ) if isinstance(UpperCamelCase__     ,				UpperCamelCase__       ) else eos_token
    __magic_name__								=			AddedToken(UpperCamelCase__     ,				lstrip=UpperCamelCase__     ,				rstrip=UpperCamelCase__       ) if isinstance(UpperCamelCase__     ,				UpperCamelCase__       ) else sep_token
    __magic_name__								=			AddedToken(UpperCamelCase__     ,				lstrip=UpperCamelCase__     ,				rstrip=UpperCamelCase__       ) if isinstance(UpperCamelCase__     ,				UpperCamelCase__       ) else cls_token
    __magic_name__								=			AddedToken(UpperCamelCase__     ,				lstrip=UpperCamelCase__     ,				rstrip=UpperCamelCase__       ) if isinstance(UpperCamelCase__     ,				UpperCamelCase__       ) else unk_token
    __magic_name__								=			AddedToken(UpperCamelCase__     ,				lstrip=UpperCamelCase__     ,				rstrip=UpperCamelCase__       ) if isinstance(UpperCamelCase__     ,				UpperCamelCase__       ) else pad_token

    # Mask token behave like a normal word, i.e. include the space before it
    __magic_name__								=			AddedToken(UpperCamelCase__     ,				lstrip=UpperCamelCase__     ,				rstrip=UpperCamelCase__       ) if isinstance(UpperCamelCase__     ,				UpperCamelCase__       ) else mask_token

    super().__init__(
        errors=UpperCamelCase__     ,				bos_token=UpperCamelCase__     ,				eos_token=UpperCamelCase__     ,				unk_token=UpperCamelCase__     ,				sep_token=UpperCamelCase__     ,				cls_token=UpperCamelCase__     ,				pad_token=UpperCamelCase__     ,				mask_token=UpperCamelCase__     ,				add_prefix_space=UpperCamelCase__     ,				**UpperCamelCase__     ,				)

    with open(UpperCamelCase__     ,				encoding="""utf-8"""       ) as vocab_handle:
      __magic_name__								=			json.load(UpperCamelCase__       )
    __magic_name__								=			{v: k for k, v in self.encoder.items()}
    __magic_name__								=			errors  # how to handle errors in decoding
    __magic_name__								=			bytes_to_unicode()
    __magic_name__								=			{v: k for k, v in self.byte_encoder.items()}
    with open(UpperCamelCase__     ,				encoding="""utf-8"""       ) as merges_handle:
      __magic_name__								=			merges_handle.read().split("""\n"""       )[1:-1]
    __magic_name__								=			[tuple(merge.split()       ) for merge in bpe_merges]
    __magic_name__								=			dict(zip(UpperCamelCase__     ,				range(len(UpperCamelCase__       )       )       )       )
    __magic_name__								=			{}
    __magic_name__								=			add_prefix_space

    # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
    __magic_name__								=			re.compile(R"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""       )
  @property
  def 				_lowercase				(					self     :					Dict       )     ->      Tuple:







    """simple docstring"""






    return len(self.encoder       )
  def 				_lowercase				(					self     :					str       )     ->      Dict:







    """simple docstring"""






    return dict(self.encoder     ,				**self.added_tokens_encoder       )
  def 				_lowercase				(					self     :					Optional[int]     ,				UpperCamelCase__     :					Any       )     ->      Optional[int]:







    """simple docstring"""






    if token in self.cache:
      return self.cache[token]
    __magic_name__								=			tuple(UpperCamelCase__       )
    __magic_name__								=			get_pairs(UpperCamelCase__       )

    if not pairs:
      return token

    while True:
      __magic_name__								=			min(UpperCamelCase__     ,				key=lambda UpperCamelCase__       : self.bpe_ranks.get(UpperCamelCase__     ,				float("""inf"""       )       )       )
      if bigram not in self.bpe_ranks:
        break
      __magic_name__	,       __magic_name__								=			bigram
      __magic_name__								=			[]
      __magic_name__								=			0
      while i < len(UpperCamelCase__       ):
        try:
          __magic_name__								=			word.index(UpperCamelCase__     ,				UpperCamelCase__       )
        except ValueError:
          new_word.extend(word[i:]       )
          break
        else:
          new_word.extend(word[i:j]       )
          __magic_name__								=			j

        if word[i] == first and i < len(UpperCamelCase__       ) - 1 and word[i + 1] == second:
          new_word.append(first + second       )
          i += 2
        else:
          new_word.append(word[i]       )
          i += 1
      __magic_name__								=			tuple(UpperCamelCase__       )
      __magic_name__								=			new_word
      if len(UpperCamelCase__       ) == 1:
        break
      else:
        __magic_name__								=			get_pairs(UpperCamelCase__       )
    __magic_name__								=			""" """.join(UpperCamelCase__       )
    __magic_name__								=			word
    return word
  def 				_lowercase				(					self     :					List[Any]     ,				UpperCamelCase__     :					str       )     ->      str:







    """simple docstring"""






    __magic_name__								=			[]
    for token in re.findall(self.pat     ,				UpperCamelCase__       ):
      __magic_name__								=			"""""".join(
          self.byte_encoder[b] for b in token.encode("""utf-8"""       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
      bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase__       ).split(""" """       )       )
    return bpe_tokens
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					List[Any]       )     ->      Any:







    """simple docstring"""






    return self.encoder.get(UpperCamelCase__     ,				self.encoder.get(self.unk_token       )       )
  def 				_lowercase				(					self     :					str     ,				UpperCamelCase__     :					List[str]       )     ->      str:







    """simple docstring"""






    return self.decoder.get(UpperCamelCase__       )
  def 				_lowercase				(					self     :					Any     ,				UpperCamelCase__     :					int       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			"""""".join(UpperCamelCase__       )
    __magic_name__								=			bytearray([self.byte_decoder[c] for c in text]       ).decode("""utf-8"""     ,				errors=self.errors       )
    return text
  def 				_lowercase				(					self     :					int     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Optional[str] = None       )     ->      Tuple[str]:







    """simple docstring"""






    if not os.path.isdir(UpperCamelCase__       ):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
      return
    __magic_name__								=			os.path.join(
        UpperCamelCase__     ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )
    __magic_name__								=			os.path.join(
        UpperCamelCase__     ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]       )

    with open(UpperCamelCase__     ,				"""w"""     ,				encoding="""utf-8"""       ) as f:
      f.write(json.dumps(self.encoder     ,				indent=2     ,				sort_keys=UpperCamelCase__     ,				ensure_ascii=UpperCamelCase__       ) + """\n"""       )

    __magic_name__								=			0
    with open(UpperCamelCase__     ,				"""w"""     ,				encoding="""utf-8"""       ) as writer:
      writer.write("""#version: 0.2\n"""       )
      for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,				key=lambda UpperCamelCase__       : kv[1]       ):
        if index != token_index:
          logger.warning(
              F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
              """ Please check that the tokenizer is not corrupted!"""       )
          __magic_name__								=			token_index
        writer.write(""" """.join(UpperCamelCase__       ) + """\n"""       )
        index += 1

    return vocab_file, merge_file
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					List[int]     ,				UpperCamelCase__     :					Optional[List[int]] = None       )     ->      List[int]:







    """simple docstring"""






    if token_ids_a is None:
      return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    __magic_name__								=			[self.cls_token_id]
    __magic_name__								=			[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep
  def 				_lowercase				(					self     :					Any     ,				UpperCamelCase__     :					List[int]     ,				UpperCamelCase__     :					Optional[List[int]] = None     ,				UpperCamelCase__     :					bool = False       )     ->      List[int]:







    """simple docstring"""






    if already_has_special_tokens:
      return super().get_special_tokens_mask(
          token_ids_a=UpperCamelCase__     ,				token_ids_a=UpperCamelCase__     ,				already_has_special_tokens=UpperCamelCase__       )

    if token_ids_a is None:
      return [1] + ([0] * len(UpperCamelCase__       )) + [1]
    return [1] + ([0] * len(UpperCamelCase__       )) + [1, 1] + ([0] * len(UpperCamelCase__       )) + [1]
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					List[int]     ,				UpperCamelCase__     :					Optional[List[int]] = None       )     ->      List[int]:







    """simple docstring"""






    __magic_name__								=			[self.sep_token_id]
    __magic_name__								=			[self.cls_token_id]

    if token_ids_a is None:
      return len(cls + token_ids_a + sep       ) * [0]
    return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]





  def 				_lowercase				(					self     :					List[str]     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					Dict=False     ,				**UpperCamelCase__     :					Tuple       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			kwargs.pop("""add_prefix_space"""     ,				self.add_prefix_space       )
    if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase__       ) > 0 and not text[0].isspace()):
      __magic_name__								=			""" """ + text
    return (text, kwargs)



import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD




class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						1
  a__       			=						True
  a__       			=						False
  a__       			=						False
  a__       			=						False
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			[]
    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=UpperCamelCase__     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

      __magic_name__								=			FlaxTransformeraDModel(
          in_channels=self.out_channels     ,				n_heads=self.num_attention_heads     ,				d_head=self.out_channels // self.num_attention_heads     ,				depth=1     ,				use_linear_projection=self.use_linear_projection     ,				only_cross_attention=self.only_cross_attention     ,				use_memory_efficient_attention=self.use_memory_efficient_attention     ,				dtype=self.dtype     ,				)
      attentions.append(UpperCamelCase__       )

    __magic_name__								=			resnets
    __magic_name__								=			attentions

    if self.add_downsample:
      __magic_name__								=			FlaxDownsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					List[Any]     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					Optional[int]=True       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			()

    for resnet, attn in zip(self.resnets     ,				self.attentions       ):
      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      __magic_name__								=			attn(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      output_states += (hidden_states,)

    if self.add_downsample:
      __magic_name__								=			self.downsamplers_a(UpperCamelCase__       )
      output_states += (hidden_states,)

    return hidden_states, output_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						True
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					str       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=UpperCamelCase__     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )
    __magic_name__								=			resnets

    if self.add_downsample:
      __magic_name__								=			FlaxDownsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					str     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					int=True       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			()

    for resnet in self.resnets:
      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      output_states += (hidden_states,)

    if self.add_downsample:
      __magic_name__								=			self.downsamplers_a(UpperCamelCase__       )
      output_states += (hidden_states,)

    return hidden_states, output_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						1
  a__       			=						True
  a__       			=						False
  a__       			=						False
  a__       			=						False
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			[]
    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if (i == self.num_layers - 1) else self.out_channels
      __magic_name__								=			self.prev_output_channel if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=resnet_in_channels + res_skip_channels     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

      __magic_name__								=			FlaxTransformeraDModel(
          in_channels=self.out_channels     ,				n_heads=self.num_attention_heads     ,				d_head=self.out_channels // self.num_attention_heads     ,				depth=1     ,				use_linear_projection=self.use_linear_projection     ,				only_cross_attention=self.only_cross_attention     ,				use_memory_efficient_attention=self.use_memory_efficient_attention     ,				dtype=self.dtype     ,				)
      attentions.append(UpperCamelCase__       )

    __magic_name__								=			resnets
    __magic_name__								=			attentions

    if self.add_upsample:
      __magic_name__								=			FlaxUpsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					Optional[Any]     ,				UpperCamelCase__     :					Dict=True       )     ->      Union[str, Any]:







    """simple docstring"""






    for resnet, attn in zip(self.resnets     ,				self.attentions       ):
      # pop res hidden states
      __magic_name__								=			res_hidden_states_tuple[-1]
      __magic_name__								=			res_hidden_states_tuple[:-1]
      __magic_name__								=			jnp.concatenate((hidden_states, res_hidden_states)     ,				axis=-1       )

      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      __magic_name__								=			attn(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )

    if self.add_upsample:
      __magic_name__								=			self.upsamplers_a(UpperCamelCase__       )

    return hidden_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						42
  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						True
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					List[Any]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			[]

    for i in range(self.num_layers       ):
      __magic_name__								=			self.in_channels if (i == self.num_layers - 1) else self.out_channels
      __magic_name__								=			self.prev_output_channel if i == 0 else self.out_channels

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=resnet_in_channels + res_skip_channels     ,				out_channels=self.out_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

    __magic_name__								=			resnets

    if self.add_upsample:
      __magic_name__								=			FlaxUpsampleaD(self.out_channels     ,				dtype=self.dtype       )





  def __call__(					self     :					List[Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					Optional[Any]     ,				UpperCamelCase__     :					str=True       )     ->      Dict:







    """simple docstring"""






    for resnet in self.resnets:
      # pop res hidden states
      __magic_name__								=			res_hidden_states_tuple[-1]
      __magic_name__								=			res_hidden_states_tuple[:-1]
      __magic_name__								=			jnp.concatenate((hidden_states, res_hidden_states)     ,				axis=-1       )

      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )

    if self.add_upsample:
      __magic_name__								=			self.upsamplers_a(UpperCamelCase__       )

    return hidden_states







class 			UpperCAmelCase_     (					nn.Module	):


  '''simple docstring'''







  a__       			=						42
  a__       			=						0.0
  a__       			=						1
  a__       			=						1
  a__       			=						False
  a__       			=						False
  a__       			=						jnp.floataa
  def 				_lowercase				(					self     :					List[Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			[
        FlaxResnetBlockaD(
            in_channels=self.in_channels     ,				out_channels=self.in_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
    ]

    __magic_name__								=			[]

    for _ in range(self.num_layers       ):
      __magic_name__								=			FlaxTransformeraDModel(
          in_channels=self.in_channels     ,				n_heads=self.num_attention_heads     ,				d_head=self.in_channels // self.num_attention_heads     ,				depth=1     ,				use_linear_projection=self.use_linear_projection     ,				use_memory_efficient_attention=self.use_memory_efficient_attention     ,				dtype=self.dtype     ,				)
      attentions.append(UpperCamelCase__       )

      __magic_name__								=			FlaxResnetBlockaD(
          in_channels=self.in_channels     ,				out_channels=self.in_channels     ,				dropout_prob=self.dropout     ,				dtype=self.dtype     ,				)
      resnets.append(UpperCamelCase__       )

    __magic_name__								=			resnets
    __magic_name__								=			attentions





  def __call__(					self     :					Optional[int]     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					List[Any]     ,				UpperCamelCase__     :					int=True       )     ->      str:







    """simple docstring"""






    __magic_name__								=			self.resnets[0](UpperCamelCase__     ,				UpperCamelCase__       )
    for attn, resnet in zip(self.attentions     ,				self.resnets[1:]       ):
      __magic_name__								=			attn(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )
      __magic_name__								=			resnet(UpperCamelCase__     ,				UpperCamelCase__     ,				deterministic=UpperCamelCase__       )

    return hidden_states

